Much of the drinking water in this country, as well as water for irrigation or other uses, comes from—or comes in contact with—natural waters that include rivers, streams, and ground water. Various metals are among the types of contaminants found in natural waters. Monitoring for the presence of metals—such as arsenic, copper, cadmium, mercury, and lead, among others—is desirable and essential. Although monitoring is possible, conventional monitoring techniques are not well automated, or scalable. Rather, these require manual steps which are not only labor- and time-intensive, but also expensive to carry out.
Conversely, labor, time, and cost can be reduced in proportion to the ability to monitor water quality in a manner that is substantially automated. A desirable level of automation would involve keeping the monitoring steps independent of continuous operator intervention, and without the need for manual calibration.
There have been prior techniques for monitoring water quality and analyzing for contaminants. Some have employed electrochemical approaches that involve the detection and analysis of signals associated with a metal-containing sample in the presence of an electrolyte and an electrode or an electrode array, such as when a metal is deposited on an electrode or stripped from an electrode under a known potential. Because stripping is the reverse of the deposition process, absolute charge for deposition process (Qdep) generally equals that of stripping (Qstrip). Such techniques can be configured to detect the presence of metals, and to characterize them based on differences in signals associated with the electrical current needed to cause deposition or stripping, as the case may be depending on how the test is set up. One example of this is seen in efforts that have focused on stripping analysis, which is one of the more sensitive of the electrochemical analytical techniques for metals. Problematically, at least from a perspective of scalability, many prior stripping methods for metal determination require a high level of operator intervention and experience, largely owing to the variable nature of the electrode surface/electrolyte interface and the necessity for having an operator perform blank subtraction and calibration.
Consequently, prior systems and techniques have not been able to achieve the needed level of automation, or scalability, because the signals depend on multiple variables that change over time or even over the course of a day. These variables include the temperature and humidity of the testing environment, the viscosity of the sample, and other factors that can affect mass transport rates of metals and metal-containing particles in a sample. Consequently, the accuracy of prior approaches depends on regular calibration of the sensors and analytical instrumentation.
Moreover, in the natural state, the metal(s) of interest are typically dissolved in the water or other liquid sample being tested. Not only does the metal(s) dissolved in this solvent produce a signal in response to electrochemical events, but the solvents also produce background signals that must be corrected for. Consequently, prior approaches have included the use of separate, blank electrolytes from which metals have been purged. Although such approaches allow for background correction, this again increases labor, time, and costs associated with monitoring of samples. An operator must be present on site who will run the analytical test on the blank, then run the test again on the sample, so that the signal(s) attributable to analyte(s) of interest can be determined. Because the environment within or leading to the analytical instrument might vary widely in terms of temperature, humidity, pH, viscosity, and other variables, the need for calibration will be frequent, and time-consuming.
Further, not only does the testing environment affect the uniformity of results, but the condition of the working electrode changes over time, as well. Fouling of the working electrode through natural deposits and particulates such as silt and organic matter result in passivation of the electrode surface, increases the time required for analytes of interest to diffuse to the electrode surface, and diminishes the quality, consistency, or both of the signal required to make the necessary determinations.
Unfortunately, the likelihood for inaccuracies and inconsistent performance over time, which previously was associated with automated monitoring for metals and other contaminants in water, has prompted a decrease in the level of automation, with a corresponding increase in human operator involvement in the testing process. This, inevitably, results in less frequent testing and fewer testing centers. It also means that delays are inherent in that sampling and testing occur in separate places, and at different times—often hours or days apart. Conversely, there is a significant and long-felt need to increase testing frequency and regularity, and to facilitate decentralized testing at remote locations, in a manner that requires less operator involvement. Likewise, the need exists for accurate detection and determination of metals in water and other liquid samples that may contain dissolved metals in them. Additionally, the need exists to reduce operator involvement and increase automation, storing testing data that can be communicated from a remote location in real time. Preferably, these capabilities are to occur in a time frame conducive to high throughput (e.g., 1-2 minutes or less) while being performed remotely, and without operator involvement and without need for ongoing manual calibration. The need exists not only to analyze for a single metal ion, but to test actual real-world samples and distinguish between various metal atoms or species that might be found in them. In short, there is a widely recognized need for automated, compact, remotely deployed sensor networks for determination of metal content (both detection and quantification) in liquid samples.
One advantage of the present embodiments rests in the fact that, unlike other electrochemical analytical methods, the acquired signals—which are obtained by exhaustive coulometry as explained further—are not substantially dependent on variables that affect mass transport rates within the testing medium. Furthermore, the signals acquired by practicing present embodiments are not as significantly vulnerable to changes to the working electrode surface that necessarily occur over time, such as fouling. This reduces the need to change the sensors as frequently as would otherwise be the case. Another advantage is the time-, labor-, and costs—savings by the automated nature and the reduction in time between sample collection and analysis. All of these objects, and additional ones, are met based on the disclosures contained herein as claimed according to these multiple embodiments, and their alternatives.